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murphy k probabilistic machine learning advanceics 2023

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Textbook in PDF format An advanced book for researchers and graduate students working in Machine Learning and statistics who want to learn about Deep Learning, Bayesian inference, generative models, and decision making under uncertainty. An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in Machine Learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts Deep Learning into a larger statistical context and unifies approaches based on Deep Learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in Machine Learning. We assume the reader has some prior exposure to ML and other relevant mathematical topics (e.g., probability, statistics, linear algebra, optimization). Python code (mostly in JAX) to reproduce nearly all of the figures can be found online. Clicking on the notebook link will open it inside Google Colab, which will let you easily reproduce the figure for yourself, and modify the underlying source code to gain a deeper understanding of the methods. (Colab gives you access to a free GPU, which is useful for some of the more computationally heavy demos.) In addition to the online code, probml.github.io/supp contains some additional supplementary online content which was excluded from the main book for space reasons. Introduction I Fundamentals Probability Statistics Graphical models Information theory Optimization II Inference Inference algorithms: an overview Gaussian filtering and smoothing Message passing algorithms Variational inference Monte Carlo methods Markov chain Monte Carlo Sequential Monte Carlo III Prediction Predictive models: an overview Generalized linear models Deep neural networks Bayesian neural networks Gaussian processes Beyond the iid assumption IV Generation Generative models: an overview Variational autoencoders Autoregressive models Normalizing flows Energy-based models Diffusion models Generative adversarial networks V Discovery Discovery methods: an overview Latent factor models State-space models Graph learning Nonparametric Bayesian models Representation learning Interpretability IV Action Decision making under uncertainty Reinforcement learning Causality

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Murphy K. Probabilistic Machine Learning. Advanced Topics 2023.pdf 38.9 MB

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murphy k probabilistic machine learning advanceics 2023

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